Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, D-40225 Dusseldorf, Germany.
Magn Reson Imaging. 2012 Dec;30(10):1468-74. doi: 10.1016/j.mri.2012.04.025. Epub 2012 Jul 20.
To evaluate which mathematical model (monoexponential, biexponential, statistical, kurtosis) fits best to the diffusion-weighted signal in prostate magnetic resonance imaging (MRI).
24 prostate 3-T MRI examinations of young volunteers (YV, n=8), patients with biopsy proven prostate cancer (PC, n=8) and an aged matched control group (AC, n=8) were included. Diffusion-weighted imaging was performed using 11 b-values ranging from 0 to 800 s/mm(2).
Monoexponential apparent diffusion coefficient (ADC) values were significantly (P<.001) lower in the peripheral (PZ) zone (1.18±0.16 mm(2)/s) and the central (CZ) zone (0.73±0.13 mm(2)/s) of YV compared to AC (PZ 1.92±0.17 mm(2)/s; CZ 1.35±0.21 mm(2)/s). In PC ADC(mono) values (0.61±0.06 mm(2)/s) were significantly (P<.001) lower than in the peripheral of central zone of AC. Using the statistical analysis (Akaike information criteria) in YV most pixels were best described by the biexponential model (82%), the statistical model, respectively kurtosis (93%) each compared to the monoexponential model. In PC the majority of pixels was best described by the monoexponential model (57%) compared to the biexponential model.
Although a more complex model might provide a better fitting when multiple b-values are used, the monoexponential analyses for ADC calculation in prostate MRI is sufficient to discriminate prostate cancer from normal tissue using b-values ranging from 0 to 800 s/mm(2).
评估哪种数学模型(单指数、双指数、统计、峰度)最适合前列腺磁共振成像(MRI)中的扩散加权信号。
本研究纳入了 24 例前列腺 3T MRI 检查的年轻志愿者(YV,n=8)、经活检证实患有前列腺癌的患者(PC,n=8)和年龄匹配的对照组(AC,n=8)。使用 11 个 b 值(范围为 0 至 800 s/mm2)进行扩散加权成像。
YV 的外周区(PZ)和中央区(CZ)的单指数表观扩散系数(ADC)值(分别为 1.18±0.16 mm2/s 和 0.73±0.13 mm2/s)显著低于 AC(PZ 为 1.92±0.17 mm2/s;CZ 为 1.35±0.21 mm2/s)(P<.001)。PC 的 ADC(单指数)值(0.61±0.06 mm2/s)显著低于 AC 的外周区和中央区(P<.001)。在 YV 中,使用统计分析(Akaike 信息准则),大多数像素最好用双指数模型(82%)、统计模型(93%)描述,而不是单指数模型。在 PC 中,与双指数模型相比,大多数像素最好用单指数模型(57%)描述。
尽管在使用多个 b 值时,更复杂的模型可能提供更好的拟合,但在前列腺 MRI 中,使用 ADC 计算的单指数分析足以在 0 至 800 s/mm2 的 b 值范围内区分前列腺癌与正常组织。